Experiments on natural and synthetic events validate our approach. For validating performance on real event-camera recordings, we adopt the recent DHP19 dataset [1]}. DHP19 provides recordings of 33 activities from four different points of view. Despite the excellent contribution to event-based vision, DHP19 provides few self-occlusions or hard situations, as most of the activities are conducted on the spot. To fill these gaps, we propose a new, challenging event-based dataset for Human Pose Estimation by simulating events from the standard Human3.6M dataset [2]}. The event-camera community proposed numerous simulation tools to tackle the absence of data [3]}, and these solutions have been successfully adopted in recent work [4]}. Human3.6m provides challenging scenarios, such as people walking and moving extensively in the scene, that are intrinsically harder. In fact, we test our proposal on both DHP19 and Event-Human3.6 and provide different ablations and experiments to support our claims. To summarize, our proposal consists of three main contributions:
